Posts in category: nVidia AI

The increased frequency and severity of extreme weather and climate events could take a million lives and cost $1.7 trillion annually by 2050, according to the Munich Reinsurance Company.

This underscores a critical need for accurate weather forecasting, especially with the rise in severe weather occurrences such as blizzards, hurricanes and heatwaves. AI and accelerated computing are poised to help.

More than 180 weather modeling centers employ robust high performance computing (HPC) infrastructure to crunch traditional numerical weather prediction (NWP) models. These include the European Center for Medium-Range Weather Forecasts (ECMWF), which operates on 983,040 CPU cores, and the U.K. Met Office’s supercomputer, which uses more than 1.5 million CPU cores and consumes 2.7 megawatts of power.

Rethinking HPC Design

The global push toward energy efficiency is urging a rethink of HPC system design. Accelerated computing, harnessing the power of GPUs, offers a promising, energy-efficient alternative that speeds up computations.

NVIDIA GPUs have made a significant impact on globally adopted weather models, including those from ECMWF, the Max Planck Institute for Meteorology, the German Meteorological Service and the National Center for Atmospheric Research.

GPUs enhance performance up to 24x, improve energy efficiency, and reduce costs and space requirements.

“To make reliable weather predictions and climate projections a reality within power budget limits, we rely on algorithmic improvements and hardware where NVIDIA GPUs are an alternative to CPUs,” said Oliver Fuhrer, head of numerical prediction at MeteoSwiss, the Swiss national office of meteorology and climatology.

AI Model Boosts Speed, Efficiency

NVIDIA’s AI-based weather-prediction model FourCastNet offers competitive accuracy with orders of magnitude greater speed and energy efficiency compared with traditional methods. FourCastNet rapidly produces week-long forecasts and allows for the generation of large ensembles — or groups of models with slight variations in starting conditions — for high-confidence, extreme weather predictions.

For example, based on historical data, FourCastNet accurately predicted the temperatures on July 5, 2018, in Ouargla, Algeria — Africa’s hottest recorded day.

Using NVIDIA GPUs, FourCastNet quickly and accurately generated 1,000 ensemble members, outpacing traditional models. A dozen of the members accurately predicted the high temperatures in Algeria based on data from three weeks before it occurred.

This marked the first time the FourCastNet team predicted a high-impact event weeks in advance, demonstrating AI’s potential for reliable weather forecasting with lower energy consumption than traditional weather models.

FourCastNet uses the latest AI advances, such as transformer models, to bridge AI and physics for groundbreaking results. It’s about 45,000x faster than traditional NWP models. And when trained, FourCastNet consumes 12,000x less energy to produce a forecast than the Europe-based Integrated Forecast System, a gold-standard NWP model.

“NVIDIA FourCastNet opens the door to the use of AI for a wide variety of applications that will change the shape of the NWP enterprise,” said Bjorn Stevens, director of the Max Planck Institute for Meteorology.

Expanding What’s Possible

In an NVIDIA GTC session, Stevens described what’s possible now with the ICON climate research tool. The Levante supercomputer, using 3,200 CPUs, can simulate 10 days of weather in 24 hours, Stevens said. In contrast, the JUWELS Booster supercomputer, using 1,200 NVIDIA A100 Tensor Core GPUs, can run 50 simulated days in the same amount of time.

Scientists are looking to study climate effects 300 years into the future, which means systems need to be 20x faster, Stevens added. Embracing faster technology like NVIDIA H100 Tensor Core GPUs and simpler code could get us there, he said.

Researchers now face the challenge of striking the optimal balance between physical modeling and machine learning to produce faster, more accurate climate forecasts. A ECMWF blog published last month describes this hybrid approach, which relies on machine learning for initial predictions and physical models for data generation, verification and system refinement.

Such an integration — delivered with accelerated computing — could lead to significant advancements in weather forecasting and climate science, ushering in a new era of efficient, reliable and energy-conscious predictions.

Learn more about how accelerated computing and AI boost climate science through these resources:

• NVIDIA one-pager: Predicting the Weather With AI
• NVIDIA one-pager: Faster Weather Predictions
• NVIDIA resource page: Sustainable Computing
• NVIDIA technical blog: AI for a Scientific Computing Revolution

AI and accelerated computing will help climate researchers achieve the miracles they need to achieve breakthroughs in climate research, NVIDIA founder and CEO Jensen Huang said during a keynote Monday at the Berlin Summit for the Earth Virtualization Engines initiative.

“Richard Feynman once said that “what I can’t create, I don’t understand” and that’s the reason why climate modeling is so important,” Huang told 180 attendees at the Harnack House in Berlin, a storied gathering place for the region’s scientific and research community.

“And so the work that you do is vitally important to policymakers to researchers to the industry,” he added.

To advance this work, the Berlin Summit brings together participants from around the globe to harness AI and high-performance computing for climate prediction.

In his talk, Huang outlined three miracles that will have to happen for climate researchers to achieve their goals, and touched on NVIDIA’s own efforts to collaborate with climate researchers and policymakers with its Earth-2 efforts.

The first miracle required will be to simulate the climate fast enough, and with a high enough resolution – on the order of just a couple of square kilometers.

The second miracle needed will be the ability to pre-compute vast quantities of data.

The third miracle needed is the ability to visualize all this data interactively with NVIDIA Omniverse to “put it in the hands of policymakers, businesses, companies, and researchers.”

The Next Wave of Climate and Weather Innovation

The Earth Virtualization Engines initiative, known as EVE, is an international collaboration that brings together digital infrastructure focused on climate science, HPC and AI aiming to provide, for the first time, easily accessible kilometer-scale climate information to sustainably manage the planet.

“The reason why Earth-2 and EVE found each other at the perfect time is because Earth-2 was based on 3 fundamental breakthroughs,” Huang said.

The initiative promises to accelerate the pace of advances, advocating coordinated climate projections at 2.5-km resolution. It’s an enormous challenge, but it’s one that builds on a huge base of advancements over the past 25 years.

A sprawling suite of applications already benefits from accelerated computing, including ICON, IFS, NEMO, MPAS, WRF-G and more — and much more computing power for such applications is coming.

The NVIDIA GH200 Grace Hopper Superchip is a breakthrough accelerated CPU designed from the ground up for giant-scale AI and high-performance computing applications.  It delivers up to 10x higher performance for applications running terabytes of data.

It’s built to scale, and by connecting large numbers of these chips together, NVIDIA can offer systems with the power efficiency to accelerate the work of researchers at the cutting edge of climate research. “To the software it looks like one giant processor,” Huang said.

To help researchers put vast quantities of data to work, quickly, to unlock understanding, Huang spoke about NVIDIA Modulus, an open-source framework for building training and fine-tuning physics-based machine learning model, and FourCastNet, a global, data-driven weather forecasting model, and how the latest AI-driven models can learn physics from real-world data.

Using raw data alone, FourCastNet is able to learn the principles governing complex weather patterns. Huang showed how FourCastNet was able to accurately predict the path of Hurricane Harvey by modeling the Coriolis force, the effect of the Earth’s rotation, on the storm.

Such models, when tethered to regular “checkpoints” created by traditional simulation, allow for more detailed, long-range forecasts. Huang then demonstrated how some of the FourCastNet ensemble’s models, running on NVIDIA GPUs, anticipated an unprecedented North African heatwave.

By running FourCastNet in Modulus, NVIDIA was able to generate 21-day weather trajectories of 1,000 ensemble members in one-tenth the time it previously took to do a single ensemble — and with 1,000x less energy consumption.

Lastly, NVIDIA technologies promise to help all this knowledge become more accessible with digital twins able to create interactive models of increasingly complex systems – from Amazon warehouses to the way 5G signals propagate in dense urban environments.

Huang then showed a stunning, high-resolution interactive visualization of global-scale climate data in the cloud, zooming in from a view of the globe to a detailed view of Berlin. This approach can work to predict climate and weather in locations as diverse as Berlin, Tokyo and Buenos Aires, Huang said.

Earth: The Final Frontier

To help meet challenges such as these, Huang outlined how NVIDIA is building more powerful systems for training AI models, simulating physical problems and interactive visualization.

“These new types of supercomputers are just coming online,” Huang said. “This is as fresh a computing technology as you can imagine.”

Huang ended his talk by thanking key researchers from across the field and playfully suggesting a mission statement for EVE.

“Earth, the final frontier, these are the voyages of EVE,” Huang said. Its “mission is to push the limits of computing in service of climate modeling, to seek out new methods and technologies to study the global-to-local state of the climate to inform today the impact of mitigation and adaptation to Earth’s tomorrow, to boldly go where no one has gone before.”

For more on Earth-2, visit https://www.nvidia.com/en-us/high-performance-computing/earth-2/

Robots are moving goods in warehouses, packaging foods and helping assemble vehicles  — when they’re not flipping burgers or serving lattes.

How did they get so skilled so fast? Robotics simulation.

Making leaps in progress, it’s transforming industries all around us.

Robotics Simulation Summarized

A robotics simulator places a virtual robot in virtual environments to test the robot’s software without requiring the physical robot. And the latest simulators can generate datasets to be used to train machine learning models that will run on the physical robots.

In this virtual world, developers create digital versions of robots, environments and other assets robots might encounter. These environments can obey the laws of physics and mimic real-world gravity, friction, materials and lighting conditions.

Who Uses Robotics Simulation? 

Robots boost operations at massive scale today. Some of the biggest and most innovative names in robots rely on robotics simulation.

Fulfillment centers handle tens of millions of packages a day, thanks to the operational efficiencies uncovered in simulation.

Amazon Robotics uses it to support its fulfillment centers. BMW Group taps into it to accelerate planning for its automotive assembly plants. Soft Robotics applies it to perfecting gripping for picking and placing foods for packaging.

Automakers worldwide are supporting their operations with robotics.

“Car companies employ nearly 14 million people. Digitalization will enhance the industry’s efficiency, productivity and speed,” said NVIDIA CEO Jensen Huang during his latest GTC keynote.

How Robotics Simulation Works, in Brief

An advanced robotics simulator begins by applying fundamental equations of physics. For example, it can use Newton’s laws of motion to determine how objects move over a small increment of time, or a timestep. It can also incorporate physical constraints of a robot, such as being composed of hinge-like joints, or being unable to pass through other objects.

Simulators use various methods to detect potential collisions between objects, identify contact points between colliding objects, and compute forces or impulses to prevent objects from passing through one another. Simulators can also compute sensor signals sought by a user, such as torques at robot joints or forces between a robot’s gripper and an object.

The simulator will then repeat this process for as many timesteps as the user requires. Some simulators — such as NVIDIA Isaac Sim, an application built on NVIDIA Omniverse — can also provide physically accurate visualizations of the simulator output at each timestep.

Using a Robotics Simulator for Outcomes

A robotics simulator user will typically import computer-aided design models of the robot and either import or generate objects of interest to build a virtual scene. A developer can use a set of algorithms to perform task planning and motion planning, and then prescribe control signals to carry out those plans. This enables the robot to perform a task and move in a particular way, such as picking up an object and placing it at a target location.

The developer can observe the outcome of the plans and control signals and then modify them as needed to ensure success. More recently, there’s been a shift toward machine learning-based methods. So instead of directly prescribing control signals, the user prescribes a desired behavior, like moving to a location without colliding. In this situation, a data-driven algorithm generates control signals based on the robot’s simulated sensor signals.

These algorithms can include imitation learning, in which human demonstrations can provide references, and reinforcement learning, where robots learn to achieve behaviors through intelligent trial-and-error, achieving years of learning quickly with an accelerated virtual experience.

Simulation Drives Breakthroughs

Simulation solves big problems. It is used to verify, validate and optimize robot designs and systems and their algorithms. Simulation also helps design facilities to be optimized for maximum efficiencies before construction or remodeling begins. This helps to reduce costly manufacturing change orders.

For robots to work safely among people, flawless motion planning is necessary. To handle delicate objects, robots need to be precise at making contact and grasping. These machines, as well as autonomous mobile robots and vehicle systems, are trained on vast sums of data to develop safe movement.

Drawing on synthetic data, simulations are enabling virtual advances that weren’t previously possible. Today’s robots born and raised in simulation will be used in the real world to solve all manner of problems.

Simulation Research Is Propelling Progress 

Driven by researchers, recent simulation advances are rapidly improving the capabilities and flexibility of robotics systems, which is accelerating deployments.

University researchers, often working with NVIDIA Research and technical teams, are solving problems in simulation that have real-world impact. Their work is expanding the potential for commercialization of new robotics capabilities across numerous markets.

Among them, robots are learning to cut squishy materials such as beef and chicken, fasten nuts and bolts for automotive assembly, as well as maneuver with collision-free motion planning for warehouses and manipulate hands with new levels of dexterity.

Such research has commercial promise across trillion-dollar industries.

High-Fidelity, Physics-Based Simulation Breakthroughs 

The ability to model physics, displayed in high resolution, ushered in the start of many industrial advances.

Researched for decades, simulations based on physics offer commercial breakthroughs today.

NVIDIA PhysX, part of Omniverse core technology, delivers high-fidelity physics-based simulations, enabling real-world experimentation in virtual environments.

PhysX enables development of the ability to assess grasp quality so that robots can learn to grasp unknown objects. PhysX is also highly capable for developing skills such as manipulation, locomotion and flight.

Launched into open source, PhysX 5 opens the doors for development of industrial applications everywhere. Today, roboticists can access PhysX as part of Isaac Sim, built on Omniverse.

The Nuts and Bolts of Assembly Simulation 

With effective grasping enabled, based on physics, the next step was to simulate more capable robotic maneuvering applicable to industries.

Assembly is a big one. It’s an essential part of building products for automotive, electronics, aerospace and medical industries. Assembly tasks include tightening nuts and bolts, soldering and welding, inserting electrical connections and routing cables.

Robotic assembly, however, is a long-standing work in progress. That’s because the physical manipulation complexity, part variability and high accuracy and reliability requirements make it extra tricky to complete successfully — even for humans.

That hasn’t stopped researchers and developers from trying, putting simulation to work in these interactions involving a lot of contact, and there are signs of progress.

NVIDIA robotics and simulation researchers in 2022 came up with a novel simulation approach to overcome the robotics assemble challenge using Isaac Sim. Their research paper, titled Factory: Fast Contact for Robotic Assembly, outlines a set of physics simulation methods and robot learning tools for achieving real-time and faster simulation for a wide range of interactions requiring lots of contact, including for assembly.

Solving the Sim-to-Real Gap for Assembly Scenarios 

Advancing the simulation work developed in the paper, researchers followed up with an effort to help solve what’s called the sim-to-real gap.

This gap is the difference between what a robot has learned in simulation and what it needs to learn to be ready for the real world.

In another paper, IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality, researchers outlined a set of algorithms, systems and tools for solving assembly tasks in simulation and transferring these skills to real robots.

NVIDIA researchers have also developed a new, faster and more efficient method for teaching robot manipulation tasks in real life scenarios — opening drawers or dispensing soap — training significantly faster than the current standard.

The research paper RVT: Robotic View Transformer for 3D Object Manipulation uses a type of neural network called a multi-view transformer to produce virtual views from the camera input.

The work combines text prompts, video input and simulation to achieve 36x faster training time than the current state of the art — reducing the time needed to teach the robot from weeks to days —  with a 26 percent improvement in the robot’s task success rate.

Robots Hands Are Grasping Dexterity 

Researchers have taken on the challenge of creating more agile hands that can work in all kinds of settings and take on new tasks.

Developers are building robotic gripping systems to pick and place items, but creating highly capable hands with human-like dexterity has so far proven too complex. Using deep reinforcement learning can require billions of labeled images, making it impractical.

NVIDIA researchers working on a project, called DeXtreme, tapped into NVIDIA Isaac Gym and Omniverse Replicator to show that it could be used to train a robot hand to quickly manipulate a cube into a desired position. Tasks like this are challenging for robotics simulators because there is a large number of contacts involved in the manipulation and because the motion has to be fast to do the manipulation in a reasonable amount of time.

The advances in hand dexterity pave the way for robots to handle tools, making them more useful in industrial settings.

The DeXtreme project, which applies the laws of physics, is capable of training robots inside its simulated universe 10,000x faster than if trained in the real world. This equates to days of training versus years.

This simulator feat shows it has the ability to model contacts, which allows a sim-to-real transfer, a holy grail in robotics for hand dexterity.

Cutting-Edge Research on Robotic Cutting

Robots that are capable of cutting can create new market opportunities.

In 2021, a team of researchers from NVIDIA, University of Southern California, University of Washington, University of Toronto and Vector Institute, and University of Sydney won “Best Student Paper” at the Robotics: Science and Systems conference. The work, titled DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting, details a “differentiable simulator” for teaching robots to cut soft materials. Previously, robots trained in this area were unreliable.

The DiSECt simulator can accurately predict the forces on a knife as it presses and slices through common biological materials.

DiSECt relies on the finite element method, which is used for solving differential equations in mathematical modeling and engineering. Differential equations show how a rate of change, or derivative, in one variable relates to others. In robotics, differential equations usually describe the relationship between forces and movement.

Applying these principles, the DiSECt project holds promise for training robots in surgery and food processing, among other areas.

Teaching Collision-Free Motion for Autonomy 

So, robotic grasping, assembling, manipulating and cutting are all making leaps. But what about autonomous mobile robots that can safely navigate?

Currently, developers can train robots for specific settings — a factory floor, fulfillment center or manufacturing plant. Within that, simulations can solve problems for specific robots, such as palette jacks, robotic arms and walking robots. Amid these chaotic setups and robot types, there are plenty of people and obstacles to avoid. In such scenes, collision-free motion generation for unknown, cluttered environments is a core component of robotics applications.

Traditional motion planning approaches that attempt to address these challenges can come up short in unknown or dynamic environments. SLAM — or simultaneous localization and mapping —  can be used to generate 3D maps of environments with camera images from multiple viewpoints, but it requires revisions when objects move and environments are changed.

To help overcome some of these shortcomings, the NVIDIA Robotics research team has co-developed with the University of Washington a new model, dubbed Motion Policy Networks (or MπNets). MπNets is an end-to-end neural policy that generates collision-free motion in real time using a continuous stream of data coming from a single fixed camera. MπNets has been trained on more than 3 million motion planning problems using a pipeline of geometric fabrics from NVIDIA Omniverse and 700 million point clouds rendered in simulation. Training it on large datasets enables navigation of unknown environments in the real world.

Apart from directly learning a trajectory model as in MπNets, the team also recently unveiled a new point cloud-based collision model called CabiNet. With the CabiNet model, one can deploy general purpose pick-and-place policies of unknown objects beyond a tabletop setup. CabiNet was trained with over 650,000 procedurally generated simulated scenes and was evaluated in NVIDIA Isaac Gym. Training with a large synthetic dataset allowed it to generalize to even out-of-distribution scenes in a real kitchen environment, without needing any real data.

Simulation Benefits to Businesses  

Developers, engineers and researchers can quickly experiment with different kinds of robot designs in virtual environments, bypassing time-consuming and expensive physical testing methods.

Applying different kinds of robot designs, in combination with robot software, to test the robot’s programming in a virtual environment before building out the physical machine reduces risks of having quality issues to fix afterwards.

While this can vastly accelerate the development timeline, it can also drastically cut costs for building and testing robots and AI models while ensuring safety.

Additionally, robot simulation helps connect robots with business systems, such as inventory databases, so a robot knows where an item is located.

Simulation of cobots, or robots working with humans, promises to reduce injuries and make jobs easier, enabling more efficient delivery of all kinds of products.

And with packages arriving incredibly fast in homes everywhere, what’s not to like.

Learn about NVIDIA Isaac Sim, Jetson Orin, Omniverse Enterprise and Metropolis.

Learn more from this Deep Learning Institute course: Introduction to Robotic Simulations in Isaac Sim

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology improves creative workflows. 

MUE Studio, founded by 3D artists Minjin Kang and Mijoo Kim, specializes in art direction, photography and 3D design for campaigns and installations. It focuses on creating unique visual identities to help clients express themselves.

The creative duo behind the studio, based in New York, said they’ve always been fascinated with blurring the boundary between fantasy and reality in their work.

Together, they created the 3D video Somewhere in the World and a summer-themed series of artwork this week In the NVIDIA Studio, using Adobe After Effects, Autodesk 3ds Max and Unreal Engine 5.

GeForce RTX 4060 graphics cards are now available to order, starting at $299. The state-of-the-art NVIDIA Ada Lovelace architecture supercharges creative apps and productivity, while delivering immersive, AI-accelerated gaming with ray tracing and DLSS 3.

Plus, Chaos Vantage 2 is now available, offering new benefits for creators on RTX GPUs — like the GeForce RTX 4060 — including NVIDIA AI Denoiser for smoother image quality, as well as the direct light reservoir sampling feature powered by NVIDIA RTX Direct Illumination (RTXDI) technology.

Chaos Vantage 2 Powered by NVIDIA RTX

Chaos Vantage is a high-quality 3D visualization tool for artists who use the V-Ray rendering software. It enables users to quickly explore and present their work in a fully ray-traced environment that can handle massive scenes based on large models. Vantage 2 adds powerful new capabilities, enabling architectural-visualization and visual-effects artists to convey their designs more effectively.

Vantage 2 features the new NVIDIA AI Denoiser, which automatically removes noise from images when rendering high-quality output. Its upscaling mode increases frame rates and responsiveness in interactive rendering for a smoother, more efficient experience in the viewport.

The update also adds direct light reservoir sampling, powered by NVIDIA RTXDI technology, enabling artists to scale multiple dynamic light sources to sizable scenes — lightning fast — with no impact on performance.

Rounding out the Vantage update are new scene states to turn design-validation presentations into interactive storyboards; support for realistic vegetation movement in the wind and interaction with animated characters; and enhancements for popular render elements like back to beauty, material and object masks for compositing in image-editing apps.

Learn more about Vantage 2, available now.

Enter the Minds of MUE Studio

When designing an environment, MUE Studio aims to create a minimalistic space that invites viewers “to enter and take a break,” said Kim.

This is the foundation of Somewhere in the World, as well as the studio’s summer-themed series of artwork. By intentionally setting the time of day, placing a specific object or choosing a remote location, the artists ensure these pieces can become a viewer’s personal path to tranquility.

“Our purpose is to provide comfort and inspire people to dream of a better world,” said Kang. “We really appreciate comments we’ve received, saying things like, ‘These images are calm,’ and ‘I would like to be present in that space.’ Such words fuel us as artists.”

The visuals’ minimalist aesthetic was brought to life through the power of NVIDIA Studio laptops equipped with GeForce RTX 3090 graphics.

The duo began sculpting and modeling in Autodesk 3ds Max. RTX-accelerated AI denoising with the default Autodesk Arnold renderer made movement in the viewport highly interactive.

MUE Studio is especially interested in the human element of their art, the founders said. Kang focuses on the artistic tension between presence and absence, and Kim explores unique human cultures throughout the pieces.

The artists completed texture application, lighting and animations in Unreal Engine 5. RTX acceleration unlocked high-fidelity interactive visualization, leading to stunning photorealistic render quality.

Kim said MUE Studio’s go-to creative app for post-production is Adobe After Effects, which includes 30+ GPU-accelerated effects. The duo applied the app’s Sharpen, Brightness and Contrast and Gradient Ramp features when putting the finishing touches on their marvelous masterpieces.

“Our series of art provides an opportunity for viewers to momentarily escape reality, creating a safe, digital space where the community can relax and interact with one another,” Kang said.

Check out more of MUE Studio’s 3D creations on Instagram.

Follow NVIDIA Studio on Instagram, Twitter and Facebook. Access tutorials on the Studio YouTube channel and get updates directly in your inbox by subscribing to the Studio newsletter.

It’s a jam-packed July with 14 newly supported titles in the GeForce NOW library, including Remnant II from Gunfire Games and Gearbox Publishing.

Need a new adventure? Check out the nine additions streaming from the cloud this week.

Plus, the Steam Summer Sale kicks off this week, and many supported titles in the GeForce NOW library are available for cheap. Keep an eye out for promotional updates right in the GeForce NOW app.

New Jams for July

The GeForce NOW library is always expanding. July brings support for 14 more titles streaming from the cloud, including Remnant II, Jagged Alliance 3, Xenonauts 2 and more.

Upgrade to a GeForce NOW Ultimate membership to play these and more than 1,600 other titles at RTX 4080 quality, with support for 4K 120 frames per second gameplay and ultrawide resolutions. Priority and Ultimate members can also play supported titles with RTX ON for real-time cinematic lighting.

Check out the full list:

• The Legend of Heroes: Trails into Reverie (New release on Steam, July 7)
• Jagged Alliance 3 (New release on Steam, July 14)
• Xenonauts 2 (Steam, July 18)
• Viewfinder (Steam, July 18)
• Techtonica (Steam, July 18)
• Remnant II (Steam, July 25)
• F1 Manager 2023 (Steam, July 31)
• Embr (Steam)
• MotoGP 23 (Steam)
• OCTOPATH TRAVELER (Steam)
• OCTOPATH TRAVELER II (Steam)
• Pro Cycling Manager 2023 (Steam)
• Riders Republic (Steam)
• Starship Troopers: Extermination (Steam)

Jump into gaming with what’s new on GeForce NOW this week:

• One Lonely Outpost (New release on Steam)
• AEW: Fight Forever (New release on Steam, June 29)
• Darkest Dungeon (Steam)
• Darkest Dungeon II (Steam)
• Derail Valley (Steam)
• Age of Empires: Definitive Edition (Steam)
• I Am Fish (Steam)
• Golf Gang (Steam)
• Contraband Police (Steam)

Juicy June

In addition to the 20 games announced in June, four extra joined GeForce NOW this month, including this week’s additions, One Lonely Outpost and AEW: Fight Forever, as well as:

• Forever Skies (Steam)
• A.V.A Global (Steam)

Age of Empires II: Definitive Edition didn’t make it in June due to technical issues. Stay tuned to GFN Thursday for more updates.

What’s on your playlist this month? Let us know your answer on Twitter or in the comments below.

Reply with a prediction of what you think will be your most played game in July.

Then come back at the end of the month and see if you were right.

— NVIDIA GeForce NOW (@NVIDIAGFN) June 28, 2023

When making financial decisions, it’s important to look at the big picture — say, one taken from a drone, satellite or AI-powered sensor.

The emerging field of spatial finance harnesses AI insights from remote sensors and aerial imagery to help banks, insurers, investment firms and businesses analyze risks and opportunities, enable new services and products, measure the environmental impact of their holdings, and assess damage after a crisis.

Spatial finance applications include monitoring assets, modeling energy efficiency, tracking emissions and pollution, detecting illegal mining and deforestation, and analyzing the risks of natural disasters. NVIDIA AI software and hardware can help the industry combine their business data with geospatial data to accelerate these applications.

By better understanding the environmental and social risks associated with an investment, the financial sector can choose to prioritize those that are more likely to support sustainable development — a framework known as environmental, social and governance (ESG).

Focus on sustainable investments is growing: A Bloomberg Intelligence analysis estimated that ESG assets will represent more than a third of total managed assets worldwide by 2025. And a report by the European Union Agency for the Space Programme predicts that the insurance and finance industry will become the top consumer of Earth observation data and services over the next decade — resulting in more than $1 billion in total revenue by 2031.

Several members of NVIDIA Inception, a global program that supports cutting-edge startups, are advancing these efforts with GPU-accelerated AI applications that can track water pollution near industrial plants, quantify the financial risk of wildfires, assess damage after storms and more.

Powerful Compute for Large-Scale Data

GPU-accelerated AI and data science can rapidly extract insights from complex, unstructured data — enabling banks and businesses to set up real-time streaming and analysis of data as it’s captured from satellites, drones, antennas and edge sensors.

By monitoring aerial imagery — available for free from public space agencies, or at higher granularity from private companies — analysts can get a clear view of how much water is being used from a reservoir over time, how many trees are being cut down for a construction project or how many homes were damaged by a tornado.

This capability can help audit investments by verifying the accuracy of written records such as government-mandated disclosures, environmental impact reports or even insurance claims.

For example, investors might track the supply chain of a company that reports it has achieved net zero in its production line, and discover that it actually relies on an overseas plant emitting coal ash visible in satellite images. Or, sensors that analyze heat emissions from buildings could help identify low-emitting businesses for a tax credit.

NVIDIA’s edge computing solutions, including the NVIDIA Jetson platform for autonomous machines and other embedded applications, are powering numerous AI initiatives in spatial finance.

In addition to using NVIDIA hardware to speed up their applications, developers are adopting software including the NVIDIA DeepStream software development kit for streaming analytics, part of the NVIDIA Metropolis platform for vision AI. They’re also using the NVIDIA Omniverse platform for building and operating metaverse applications for detailed, 3D visualizations of geospatial data.

Insuring Property — From Assessing Risks to Accelerating Claims

NVIDIA Inception members are developing GPU-accelerated applications that turn geospatial data into insights for insurance companies, reducing the number of expensive onsite visits needed to monitor the status of insured properties.

RSS-Hydro, based in Luxembourg, uses GPU computing on premises and in the cloud to train FloodSENS, a machine learning app that maps flood impact from satellite images. The company also uses NVIDIA Omniverse to animate FloodSENS in 3D, helping the team more effectively communicate flood risks and inform resource allocation planning during emergencies.

Toronto-based Ecopia AI uses deep learning-based mapping systems to mine geospatial data, helping to produce next-generation digital maps with highly accurate segmentation of buildings, roads, forests and more. These maps power diverse applications across the public and private sectors, including government climate resilience initiatives and insurance risk assessment. Ecopia uses NVIDIA GPUs to develop its AI models.

CrowdAI, based in the San Francisco Bay Area, uses deep learning tools to accelerate the insurance claims process by automatically analyzing aerial images and videos to detect assets that were damaged or destroyed in natural disasters. The company uses NVIDIA GPUs for both training and inference.

Predicting Risks and Opportunities for Businesses

Inception startups are also using geospatial data to help government groups and banks quantify the risks and opportunities of their investments — such as predicting crop yields, detecting industrial pollution and measuring the land and water use of an asset.

Switzerland-based Picterra is supporting sustainable finance with a geospatial MLOps platform that enables banks, insurance companies and financial consultancies to analyze ESG metrics. The company’s AI-driven insights can help the financial industry make investment decisions, model risk and quickly quantify vulnerabilities and opportunities in investment portfolios. The company uses NVIDIA Tensor Core GPUs and the NVIDIA CUDA Toolkit to develop its AI models, which process raw data from satellite, drone and aerial imagery.

London-based Satellite Vu, a startup applying satellite technology to address global challenges, will be able to monitor the temperature of any building on the planet in near real time using infrared camera data. These infrared images will provide its customers with insights about the economic activity, the energy efficiency of buildings, the urban heat island effect and more.

And Sourcenergy, based in Houston, uses geospatial data to power an energy supply chain intelligence platform that can help the financial services industry with market research. Its AI tools, developed using NVIDIA A100 GPUs, enable investors to independently create real-time models of energy companies’ well inventories and project costs, giving them insights even before the companies share data in their quarterly earnings reports.

Learn more about NVIDIA’s work in financial services, and read more on geospatial AI in investment management in chapter 10 of this handbook.

A trio of top scientists is helping lead one of the most ambitious efforts in the history of computing — building a digital twin of Earth.

Peter Bauer, Bjorn Stevens and Francisco “Paco” Doblas-Reyes agree that a digital twin of Earth needs to support resolutions down to a kilometer so a growing set of users can explore the risks of climate change and how to adapt to them. They say the work will require accelerated computing, AI and lots of collaboration.

Their Herculean efforts, some already using NVIDIA technologies, inspired Earth-2, NVIDIA’s contribution to the common cause.

“We will dedicate ourselves and our significant resources to direct NVIDIA’s scale and expertise in computational sciences, to join with the world’s climate science community,” Jensen Huang, founder and CEO of NVIDIA, had said when he announced the Earth-2 initiative in late 2021.

Collaborating on an Unprecedented Scale

Huang’s commitment signaled support for efforts like Destination Earth (DestinE), a pan-European project to create digital twins of the planet.

“No single computer may be enough to do it, so it needs a distributed, international effort,” said Bauer, a veteran with more than 20 years at Europe’s top weather forecasting center who now leads the project that aims to make planet-scale models available by 2030.

Last year, he co-authored a Nature article that said the work “requires collaboration on an unprecedented scale.”

In a March GTC talk, Bauer envisioned a federation that “mobilizes resources from many countries, including private players, and NVIDIA could be one that would be very interesting.”

Such resources would enable the enormous work of developing new numeric and machine-learning models, then running them in massive inference jobs to make predictions that stretch across multiple decades.

DestinE has its roots in a 2008 climate conference. It’s the fruit of a number of programs, including many Bauer led in his years with the European Centre for Medium-Range Weather Forecasts — based in Reading, England — which develops some of the most advanced weather forecast models in the world.

Consuming a Petabyte a Day

The collaboration is broad because the computing requirements are massive.

“We’re talking about producing petabytes of data a day that have to be delivered very quickly,” said Doblas-Reyes, director of the Earth sciences department at the Barcelona Supercomputing Center, a lead author at Intergovernmental Panel on Climate Change — a group that creates some of the most definitive reports on climate change — and a contributor to the DestinE program.

The digital twin effort will turn the traditional approach to weather and climate forecasting “upside down so users can be the drivers of the process,” he said in a March talk at GTC, NVIDIA’s developer conference. The goal is to “put the user at the helm of producing climate information that’s more useful for climate adaptation,” he said.

His talk described the new models, workflows and systems needed to capture in detail the chaotic nature of climate systems.

Articulating the Vision

The vision for a digital twin crystalized in a keynote at the SC20 supercomputing conference from Stevens, a director at the Max Planck Institute for Meteorology, in Hamburg. He leads work on one of the world’s top weather models for climate applications, as well as an effort to enable simulations at kilometer-level resolution, an order of magnitude finer than today’s best work.

“We need a new type of computing capability … for planetary information systems that let us work through the consequences of our actions and policies, so we can build a more sustainable future,” he said.

Stevens described a digital twin that’s accurate and interactive. For example, he imagined people querying it to see how a warming climate could affect flooding in northern Europe or food security in Africa.

AI Enables Interactive Simulations

AI will play a lead role in giving users that level of interactivity, he said in a talk at GTC last year.

“We need AI to get to where we need to be,” he said, giving shout-outs to NVIDIA and colleagues, including Bauer and Doblas-Reyes. “Real steps forward come from people bringing their different perspectives together and rethinking how we work.”

Doblas-Reyes agreed in his GTC talk this year.

“In my opinion, AI is a necessary complement for the digital twin — it’s the only way to offer true interactivity to users and help provide a good trajectory of what’s to come in our climate,” he said.

On a Journey Together

All three scientists gave examples of how NVIDIA technologies have been used in a wide variety of projects addressing climate change.

In his GTC talk, Stevens took a characteristically playful turn. He showed a cartoon version of Huang, like Isaac Newton, struck with a falling apple and an insight for how to engage with the scientific effort.

“We need you Jensen, and you need us,” Stevens said.

The MareNostrum 5 system coming to the Barcelona center provides one example. It’s expected to accelerate some of the DestinE work on NVIDIA H100 Tensor Core GPUs.

Building a digital twin of Earth is “an exciting opportunity to re-think the future of HPC with AI on top,” said Mike Pritchard, a veteran climate scientist who directs climate research at NVIDIA.

NVIDIA Omniverse for connecting 3D tools and developing metaverse applications, NVIDIA Modulus for physics-informed machine learning and NVIDIA Triton for AI inference all have roles to play in the broad effort, he said.

It’s a long and evolving collaboration, Bauer said in his GTC talk. “I sent my first email to NVIDIA on these issues 14 years ago, and NVIDIA has been with us on this journey ever since.”

To learn more, read the concept paper developed for the Berlin Summit for Earth Virtualization Engines, July 3-7, where Huang will deliver a keynote address.

It worked like magic. Computer vision algorithms running in a data center saw that a disease was about to infect a distant wheat field in India.

Sixteen days later, workers in the field found the first evidence of the outbreak.

It was the kind of wizardry people like Vinay Indraganti call digital transformation. He’s practiced it for 25 years, the last dozen of them at companies like Ingredion, a Fortune 500 food-ingredient producer.

The India project was the first big test of AGRi360 — a product suite for sustainable agriculture powered by NVIDIA Metropolis — from the startup that Indraganti co-founded, Blu Cocoon Digital.

Mobile App Taps Cloud Smarts

The pilot was both simple and effective.

Farm workers took pictures of the plants, time-stamped and geotagged by a mobile app. They sent them to the Microsoft Azure cloud, where Blu Cocoon’s custom models found patterns that enabled their uncanny prediction.

Thanks to his background in the industry, Indraganti knows the value of such timely intelligence. It can help farmers and their entire food chain of vendors reap a bumper harvest.

“It’s a vast area, that’s why we’ve made ‘AI for food’ our mantra at Blu Cocoon,” he said in an interview from the suburban Chicago office of the company headquartered in Kolkata.

A Third Eye on the Field

AGRi360 acts “like a third eye in the field,” said Pinaki Bhattacharya, a microbiologist who heads R&D at Blu Cocoon Digital.

In the pilot, it gave farmers an early warning to apply a small amount of pesticide to arrest the disease. An agrochemical company got a heads up about conditions in the area, helping it manage its supply chain.

In the future, food producers that buy the crops will get key details about their microbiology. That helps in planning exactly how and when to process the crops into products to meet the regulatory requirements where they’ll be sold.

“AGRi360 captures all these insights thanks to AI fed by pictures from farmworkers taken while they’re doing their regular jobs,” Bhattacharya said.

Evaluating Seeds and Soils

The AI models got their start in research using computer vision to quickly assess soil conditions and the quality of seeds.

Those skills are now part of the AGRi360 product portfolio along with products that monitor plant health and best practices in farming. Today, AGRi360 is in use in two countries, improving the quantity and quality of crop yields.

One customer reports it’s on track to source 100% of its products sustainably by 2025. Another saw revenues for an insecticide rise, thanks to the service.

“Our sales of Cartap 50sp grew 70% in six months thanks to AGRi360’s ability to identify emerging crop infections early,” said Vandan Churiwal, a director at Krishi Rayasan, a leading agrochemical supplier based in Kolkata.

“As a result, we’re expanding our license with Blu Cocoon to bring AI-powered insights into every area of our business,” he said.

Faster Training and Inference

Initially, the startup used CPUs to train and run its AI models. Now it exclusively uses NVIDIA GPUs and the Metropolis framework for computer vision.

“It used to take us two months to train a single AI model on CPUs,” said Indraganti. “Now, with NVIDIA A10 Tensor Core GPUs, all four models in AGRi360 can be trained in a few hours — that’s a game changer.”

The time savings add up quickly because the models need to be retrained for new crops, variants and soil types.

GPUs reduced the time to complete inference jobs, too. Predictions that require 15-20 minutes on CPUs get generated in 2-3 seconds on NVIDIA T4 Tensor Core GPUs. The speed also enables Blu Cocoon to test its models on large and growing datasets.

From Shipyards to Snack Bars

Looking ahead, Blu Cocoon is extending its work in the food supply chain into managing containers in shipyards. It’s already testing computer vision models for a customer in India.

“We’ve figured out a way to optimize movement of containers, reducing their time in the yard and minimizing touch points to save time and money,” said Indraganti.

The startup is even helping food producers create recipes with AI. It’s already cooked up a gluten-free muffin for one packaged-foods client with plant-based cheeses, shakes and snack bars next on the menu.

One customer reports the AI-powered system helped reduce the time to create a new recipe by 80%.

“We named the company Blu Cocoon Digital because we look beyond the horizon and across the ocean for ways to nurture our customers’ aspirations with digital technology — and it all runs on the NVIDIA platform and Microsoft Azure,” he said.

Read about Monarch Tractor to learn other ways AI is advancing agriculture.

Scientists at Matice Biosciences are using AI to study the regeneration of tissues in animals known as super-regenerators, such as salamanders and planarians.

The goal of the research is to develop new treatments that will help humans heal from injuries without scarring.

On the latest episode of NVIDIA’s AI Podcast, host Noah Kravtiz spoke with Jessica Whited, a regenerative biologist at Harvard University and co-founder of Matice Biosciences.

Whited was inspired to start the company after her son suffered a severe injury while riding his bike.

She realized that while her work had been dedicated ultimately to limb regeneration, the short-term byproduct of it was a wealth of information that could be used to harness this regenerative science into topical treatments that can be put in the hands of everyday people, like her son and many others, who would no longer have to live with the physical scars of their trauma.

This led her to investigate the connection between regeneration and scarring.

Whited and her team are using AI to analyze the molecular and cellular mechanisms that control regeneration and scarring in super-regenerators.

They believe that by understanding these mechanisms, they can develop new treatments to help humans heal from injuries without scarring.

To learn more about Matice, please visit www.maticebio.com or follow along on Instagram, Twitter, Facebook and LinkedIn.

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Leading users and industry-standard benchmarks agree: NVIDIA H100 Tensor Core GPUs deliver the best AI performance, especially on the large language models (LLMs) powering generative AI.

H100 GPUs set new records on all eight tests in the latest MLPerf training benchmarks released today, excelling on a new MLPerf test for generative AI. That excellence is delivered both per-accelerator and at-scale in massive servers.

For example, on a commercially available cluster of 3,584 H100 GPUs co-developed by startup Inflection AI and operated by CoreWeave, a cloud service provider specializing in GPU-accelerated workloads, the system completed the massive GPT-3-based training benchmark in less than eleven minutes.

“Our customers are building state-of-the-art generative AI and LLMs at scale today, thanks to our thousands of H100 GPUs on fast, low-latency InfiniBand networks,” said Brian Venturo, co-founder and CTO of CoreWeave. “Our joint MLPerf submission with NVIDIA clearly demonstrates the great performance our customers enjoy.”

Top Performance Available Today

Inflection AI harnessed that performance to build the advanced LLM behind its first personal AI, Pi, which stands for personal intelligence. The company will act as an AI studio, creating personal AIs users can interact with in simple, natural ways.

“Anyone can experience the power of a personal AI today based on our state-of-the-art large language model that was trained on CoreWeave’s powerful network of H100 GPUs,” said Mustafa Suleyman, CEO of Inflection AI.

Co-founded in early 2022 by Mustafa and Karén Simonyan of DeepMind and Reid Hoffman, Inflection AI aims to work with CoreWeave to build one of the largest computing clusters in the world using NVIDIA GPUs.

Tale of the Tape

These user experiences reflect the performance demonstrated in the MLPerf benchmarks announced today.

H100 GPUs delivered the highest performance on every benchmark, including large language models, recommenders, computer vision, medical imaging and speech recognition. They were the only chips to run all eight tests, demonstrating the versatility of the NVIDIA AI platform.

Excellence Running at Scale

Training is typically a job run at scale by many GPUs working in tandem. On every MLPerf test, H100 GPUs set new at-scale performance records for AI training.

Optimizations across the full technology stack enabled near linear performance scaling on the demanding LLM test as submissions scaled from hundreds to thousands of H100 GPUs.

In addition, CoreWeave delivered from the cloud similar performance to what NVIDIA achieved from an AI supercomputer running in a local data center. That’s a testament to the low-latency networking of the NVIDIA Quantum-2 InfiniBand networking CoreWeave uses.

In this round, MLPerf also updated its benchmark for recommendation systems.

The new test uses a larger data set and a more modern AI model to better reflect the challenges cloud service providers face. NVIDIA was the only company to submit results on the enhanced benchmark.

An Expanding NVIDIA AI Ecosystem

Nearly a dozen companies submitted results on the NVIDIA platform in this round. Their work shows NVIDIA AI is backed by the industry’s broadest ecosystem in machine learning.

Submissions came from major system makers that include ASUS, Dell Technologies, GIGABYTE, Lenovo, and QCT. More than 30 submissions ran on H100 GPUs.

This level of participation lets users know they can get great performance with NVIDIA AI both in the cloud and in servers running in their own data centers.

Performance Across All Workloads

NVIDIA ecosystem partners participate in MLPerf because they know it’s a valuable tool for customers evaluating AI platforms and vendors.

The benchmarks cover workloads users care about — computer vision, translation and reinforcement learning, in addition to generative AI and recommendation systems.

Users can rely on MLPerf results to make informed buying decisions, because the tests are transparent and objective. The benchmarks enjoy backing from a broad group that includes Arm, Baidu, Facebook AI, Google, Harvard, Intel, Microsoft, Stanford and the University of Toronto.

MLPerf results are available today on H100, L4 and NVIDIA Jetson platforms across AI training, inference and HPC benchmarks. We’ll be making submissions on NVIDIA Grace Hopper systems in future MLPerf rounds as well.

The Importance of Energy Efficiency

As AI’s performance requirements grow, it’s essential to expand the efficiency of how that performance is achieved. That’s what accelerated computing does.

Data centers accelerated with NVIDIA GPUs use fewer server nodes, so they use less rack space and energy. In addition, accelerated networking boosts efficiency and performance, and ongoing software optimizations bring x-factor gains on the same hardware.

Energy-efficient performance is good for the planet and business, too. Increased performance can speed time to market and let organizations build more advanced applications.

Energy efficiency also reduces costs because data centers accelerated with NVIDIA GPUs use fewer server nodes. Indeed, NVIDIA powers 22 of the top 30 supercomputers on the latest Green500 list.

Software Available to All

NVIDIA AI Enterprise, the software layer of the NVIDIA AI platform, enables optimized performance on leading accelerated computing infrastructure. The software comes with the enterprise-grade support, security and reliability required to run AI in the corporate data center.

All the software used for these tests is available from the MLPerf repository, so virtually anyone can get these world-class results.

Optimizations are continuously folded into containers available on NGC, NVIDIA’s catalog for GPU-accelerated software.

Read this technical blog for a deeper dive into the optimizations fueling NVIDIA’s MLPerf performance and efficiency.